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  <title>Yijun Lin | 林逸君</title>
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            <h1 class="display-4"> Yijun Lin | 林逸君 </h1>
            <p><br> Ph.D. Student <br>
               Computer Science Department <br>
               Viterbi School of Engineering <br>
               University of Southern California </p>
            <p class="font-italic"> Email: <a href="mailto:yijunlin@usc.edu">yijunlin@usc.edu</a></p>
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            <h5>An Explainable Deep Learning Architecture for Fine-Spatial-Scale Air Quality Prediction Using Web Data</h5>
			<p class="font-italic"> Spatiotemporal Prediction &middot; Feature Selection &middot; Semi-Supervised Learning</p>
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              <li style="margin-bottom: 8px;">Automatically select air quality-related variables from a variety of environmental factors (e.g., geographic features) using <b>L1 regularization</b> for model explainability</li>
              <li style="margin-bottom: 8px;">Model the interactions of selected features over time and space at varying spatiotemporal scopes for fine-scale air quality prediction with <b>multiple convolutional-LSTM layers</b></li>
              <li style="margin-bottom: 8px;">Add <b>semi-supervised loss</b> on neighboring predictions for overcoming the limitation of sparse labeled data</li>
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              <a href="https://github.com/linyijun/prisms-data-preprocessing" class="btn btn-outline-primary">GitHub</a>
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            <h5>Exploiting Spatiotemporal Patterns for Accurate Air Quality Forecasting using Deep Learning [SigSpatial2018 Full Paper]</h5> 
            <p class="font-italic"> Spatiotemporal Forecasting &middot; Graph Convolutional Neural Network</p>
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              <li style="margin-bottom: 8px;">Construct spatial correlation between two locations using the context of air quality-related environmental factors on a graph</li>
              <li style="margin-bottom: 8px;">Jointly model spatial and temporal dependencies using <b>a geo-context-based <a href="../publications/Diffusion convolutional recurrent neural network Data-driven traffic forecasting.pdf">diffusion-convolutional recurrent neural network</a></b> for accurately forecasting PM2.5 concentrations
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              <a href="../publications/paper_Exploiting Spatiotemporal Patterns for Accurate Air quality Forecasting using Deep Learning.pdf" class="btn btn-outline-primary">Paper</a>
              <a href="https://github.com/spatial-computing/air-quality-forecasting-model" class="btn btn-outline-primary">GitHub</a>
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            <h5>Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution [SigSpatial2017 Full Paper]</h5> 
            <p class="font-italic">Spatial Prediction &middot; Clustering  &middot; Feature Selection</p>
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              <li style="margin-bottom: 8px;">Automatically select important PM2.5-related geographic feature types (from publicly accessible data, <b>OpenStreetMap</b>) that determines the clustering pattern of PM2.5 time-series using <b>Random Forest classification</b></li>
              <li style="margin-bottom: 8px;">Build a <b>regression model</b> with the extracted features and PM2.5 observations from sparse monitoring stations for predicting PM2.5 concentrations at a fine spatial scale</li>
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              <a href="../publications/paper_Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution.pdf" class="btn btn-outline-primary">Paper</a>
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            <h5>Metrans: Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data [SigSpatial2018 Demo Paper]</h4>
            <p class="font-italic">GPS Trajectory &middot; Map Matching</p>
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              <li style="margin-bottom: 8px;">Align Los Angeles Bus GPS data to the bus scheduled trajectory data using <b>map matching approach</b> for detecting bus delay time at bus stops</li>
              <li style="margin-bottom: 8px;">Built a dashboard to show the average estimated delay time at each bus stop</li>
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              <a href="../publications/paper_Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data.pdf" class="btn btn-outline-primary">Paper</a>
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            <h4>Unlocking Maps: Text Recognition in Historical Map</h4>
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              Text Recognition<br>
              OpenCV &middot; C# &middot; SVM &middot; Grab-cut &middot; Python
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              <li>Utilized <i>Strabo</i> (a map-processing software) for the automatic extraction of text labels from historical maps</li>
              <li>Automatically evaluated <i>Strabo</i> with 15 maps to explore ways for improving the accuracy</li>
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              <a href="https://github.com/spatial-computing/strabo-text-recognition" class="btn btn-outline-primary" target="_blank">GitHub</a>
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            <h4>Movie Recommendation System Competition</h4>
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              Recommender System<br>
              Spark &middot; Spark MLlib &middot; Scala &middot; Collaborating Filtering (CF)
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              <li>Built a hybrid recommender system to predict movie ratings</li>
              <li>Achieved RMSE = 0.90 on the <a href="https://grouplens.org/datasets/movielens/" target="_blank"><i>MovieLens</i></a> dataset (20M records), comparing to a Model-based CF with RMSE = 1.22</li>
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            <h5>Mining Non-Strict Periodic Patterns in People's Trajectory</h5>
            <p class="font-italic">GPS Trajectory &middot; Clustering &middot; Mining Frequent Itemsets  &middot; Recommendation</p>
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              <li style="margin-bottom: 8px;">Cluster stationary points of users using <b>StayPoint</b> and <b>OPTICS</b> algorithm for identifying potential locations that users often visit</li>
              <li style="margin-bottom: 8px;">Mine non-strict (with a threshold) periodic patterns in people’s trajectories with multiple periods (e.g., weekly, biweekly, and monthly) using <b>a modified FP-Growth algorithm</b></li>
              <li style="margin-bottom: 8px;">Predict people's schedule and recommend nearby locations</li>
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              <a href="../projects/thesis.pdf" class="btn btn-outline-primary" target="_blank">Undergraduate thesis</a>
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            <li class="list-inline-item text-muted"> &copy; 2019 Yijun Lin</li>
            <li class="list-inline-item"><a href="https://github.com/linyijun" target="_blank" class="text-muted">GitHub</a></li>
            <li class="list-inline-item"><a href="https://www.linkedin.com/in/yijun-lin/" target="_blank" class="text-muted">LinkedIn</a></li>
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